Learning Model Trees from Data Streams

• Tipo: Artigo em Revista Científica Internacional
• Data: 2008

Autores

Ikonotnovska, E (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Lecture Notes in Computer Science

Vol. 5255

Páginas: 52-63

ISSN: 0302-9743

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 14 Citações

Scopus - 24 Citações

Outras Informações

ID Authenticus: P-005-5P3

DOI: 10.1007/978-3-540-88411-8-8

Abstract (EN): In this paper we propose a fast and incremental algorithm for learning model trees from data streams (FIMT) for regression problems. The algorithm is incremental, works online, processes examples once at the speed they arrive, and maintains an any-time regression model. The leaves contain linear-models trained online from the examples that fall at that leaf, a process with low complexity. The use of linear models in the leaves increases its any-time global performance. FIMT is able to obtain competitive accuracy with batch learners even for medium size datasets, but with better training time in an order of magnitude. We study the properties of FIMT over several artificial and real datasets and evaluate its sensitivity on the order of examples and the noise level.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Notas: 11Th International Conference on Discovery Science, OCT 13-16, 2008

Nº de páginas: 12